Learn more about using open source R for big data analysis, predictive modeling, data science and more from the staff of Revolution Analytics.

November 14, 2012

Tomorrow at 9AM 10AM Pacific, Revolution Analytics VP of Product Development Sue Ranney will introduce two key Big Data features of the new Revolution R Enterprise 6.1. Now you can train classification and regression trees on data sets of unlimited size, quickly and using the resources of multiple processors and clusters. (This white paper describes our implementation of tree models on big data.) You can also now apply the Big Data modeling algorithms to structured data in Hadoop's HDFS file system. (Here's a demo.)

New Advances in High Performance Analytics with R: ‘Big Data’ Decision Trees and Analysis of Hadoop Data

Revolution R Enterprise 6.1 includes two important advances in high performance predictive analytics with R: (1) big data decision trees, and (2) the ability to easily extract and perform predictive analytics on data stored in the Hadoop Distributed File System (HDFS).

Classification and regression trees are among the most frequently used algorithms for data analysis and data mining. The implementation provided in Revolution Analytics’ RevoScaleR package is parallelized, scalable, distributable, and designed with big data in mind.

Decision trees and all of the other high performance prediction analytics functions provided with RevoScaleR (such as linear and logistic regression, generalized linear models, and k-means clustering) can now also be used to analyze data stored in the HDFS file system. After specifying the connection parameters to the HDFS file system, some or all of the data can be directly explored, analyzed or quickly and efficiently extracted into a native file system.

In this webinar we’ll drill down into these two new capabilities and show some examples.